Tokyo-Berkeley
Data Science Boot Up Camp

July 9 - July 19, 2018





Room 056 and 052
Graduate School of Mathematical Sciences
The University of Tokyo

3-8-1 Komaba, Meguro-ku, Tokyo 153-8914, Japan

Access

Travel information

Campus map

Flyer (in Japanese)

Objectives :
Data science resides at the intersection among mathematics, statistics and computer science,
dealing with collecting and analyzing large amounts of data.
The object of the school is to introduce basic notions of statistical data analysis methods
as well as their implementation by computer software.
We will welcome students of various disciplines including mathematics, physics, astronomy,
engineering, linguistics and social science participating the school.


Supported by
Top Global University Project, MEXT Japan


Schedule
July 9 - July 13 Lecture series, Exercise sessions
July 17 - July 19 Student seminars

Lecturers will include
Philip B. Stark (University of California, Berkeley)
Yuta Koike (The University of Tokyo)


Titles and Abstracts of Lectures

Philip B. Stark
Title: Foundations of Statistics and an Introduction to Statistical Inference
Abstract:
These lectures will complement those of Prof. Koike by focusing on foundational issues in statistics, statistical inferential thinking, the interpretation of statistical calculations, and nonparametric and exact methods. Topics will include types of uncertainty; theories of probability and their shortcomings; systematic and stochastic errors; frequentist and Bayesian approaches to estimation and inference and their shortcomings; confounding; the method of comparison; the importance of experimental/observational design; assessing estimators; interpreting p-values, confidence sets, posterior probabilities, and credible sets; common fallacies in statistical inference; the Neyman model for causal inference; interference in experiments; abstract permutation methods; pseudo-random number generation; computational implementation of permutation methods and resampling methods in Python. Examples will be drawn from physical, social, and health sciences.

Yuta Koike
Title: Introduction to Statistical Data Analysis
Abstract:
In this lecture we present elementary statistical data analysis methods and their implementation by R. Starting with the basic usage of R, we explain some elementary methods from multivariate analysis such as linear regression, principal component analysis and discriminant analysis and how to implement them by R. This lecture focuses on the practical implementation of the methods rather than the theoretical details.